CN114449529A - Resource allocation optimization method and device based on mobile edge calculation and storage medium - Google Patents
Resource allocation optimization method and device based on mobile edge calculation and storage medium Download PDFInfo
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- H04W72/04—Wireless resource allocation
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Abstract
The invention discloses a resource allocation optimization method, a device and a storage medium based on mobile edge calculation, wherein the method comprises the following steps: constructing a system model based on mobile edge computing, wherein the system model comprises at least one edge server and at least one base station; calculating the transmission delay of the task at the wireless side and the local delay of the task executed locally based on the system model; calculating the unloading time delay of the task to the edge server according to the transmission time delay, the stopping probability of the user terminal and the current stopping time; establishing an optimization target and constraint conditions of the system model for minimizing total energy consumption according to the transmission delay, the local delay and the unloading delay; and calculating the optimal solution of the optimization target according to the constraint conditions to obtain the optimal resource allocation strategy of the system model. The invention considers the user mobility, the unloading strategy problem and the calculation resource allocation jointly, takes the total energy consumption of the system for processing the service as the optimization target, and can quickly obtain the resource allocation strategy.
Description
Technical Field
The present invention relates to the field of wireless communication technologies, and in particular, to a resource allocation optimization method and apparatus based on mobile edge computing, and a storage medium.
Background
Due to the limited computing power and battery capacity of mobile equipment, mobile cloud computing is produced in order to solve the problem of insufficient computing power of local terminals caused by computing-intensive applications brought by the 5G era. The mobile cloud computing means that part or all of tasks of the mobile terminal are unloaded to a cloud server, so that the problem of insufficient computing power is solved. However, this method has a limitation on backhaul resources, and the offloaded task has a high delay and does not meet the requirements of the urrllc task. Then, some researchers proposed mobile edge computing to solve the problem of user delay and limited computing resources by offloading applications to edge servers near the user side.
However, since the conventional offload management and the user mobility management are separated, that is, the task offload does not consider the problem of user mobility, so that the final decision result often deviates from the optimal value of the system, a new mechanism is urgently needed to be found to optimize the offload policy of user mobility, so as to reduce the network handover cost, improve the network capacity, and form the optimal configuration of network resources.
Disclosure of Invention
The technical problem to be solved by the embodiments of the present invention is to provide a resource allocation optimization method, device and storage medium based on mobile edge computing, which jointly consider the user mobility, the offloading policy problem and the computing resource allocation, and can quickly obtain a resource allocation policy by taking the minimum total energy consumption of system processing services as an optimization target.
In order to achieve the above object, an embodiment of the present invention provides a resource allocation optimization method based on mobile edge calculation, including:
constructing a system model based on mobile edge calculation, wherein the system model comprises at least one edge server and at least one base station, and the edge server and the base station provide calculation and communication services for all user terminals in the coverage range of the edge server and the base station;
calculating the transmission delay of the task at the wireless side and the local delay of the task executed locally based on the system model;
calculating the unloading time delay of the task to the edge server according to the transmission time delay, the stopping probability of the user terminal and the current stopping time;
establishing an optimization target and a constraint condition of the system model with minimized total energy consumption according to the transmission delay, the local delay and the unloading delay;
and calculating the optimal solution of the optimization target according to the constraint condition to obtain the optimal resource allocation strategy of the system model.
As an improvement of the above scheme, each task triplet generated by the user terminal
Is shown in which DiIndicating the size of the task, CiIndicating the computing resources required by the task, tmaxiRepresenting the maximum tolerant delay of the task;
with the variable a being 0-1ijRepresents the way tasks are executed, namely:
wherein the content of the first and second substances,
the task i can only select a candidate edge server set Br in the system to unload or select local unloading; the set V {1,2, …, V } represents all user terminals within the coverage of the edge server and the base station; the set a {1,2, …, m } represents all edge servers of the entire system.
As an improvement of the above scheme, the calculating, based on the system model, a transmission delay of a task on a wireless side and a local delay of a task executed locally specifically includes:
acquiring bandwidth B and Gaussian white noise power N of an uplink channel in the system model0The transmitting power G of the user terminal, the channel fading factor h of an uplink and the average distance d from the beginning to the end of the transmission task of the user terminal;
according to the channel fading factor h and the average distance
And formulas
Calculating to obtain a channel gain parameter Hi；
According to the bandwidth B and the Gaussian white noise power N0The transmission power G, the channel gain parameter HiAnd formulas
Calculating to obtain the average data transmission rate r of the taski；
According to task size DiThe average data transmission rate riAnd formulas
Calculating to obtain the transmission time delay of the task at the wireless side
According to the computing resources C required by the taskiProcessing capacity f of the user terminaliAnd formulas
Calculating local time delay of local execution of task
As an improvement of the above scheme, the calculating, according to the transmission delay, the staying probability of the user terminal, and the current staying time, the offloading delay of the task to the edge server specifically includes:
the normal distribution probability density function f (x) is adopted to express the preference of the stay time of the user terminal in a certain area, namely
Wherein, mucDenotes the mean value, δcRepresents the variance;
obtaining the staying probability of the user terminal in a certain area at a certain moment according to the normal distribution probability density function f (x)
Wherein x is1Is x2The last time of (c);
according to the current moving speed v of the user terminaliThe distance l from the position of the user terminal in the coverage range of a certain edge server to the position of the user terminal out of the coverage range of the certain edge server and a formula
Calculating to obtain the current stay time of the user terminal at the position
According to the stay probability and the current stay time
Calculating to obtain a weighting factor of the computing resource distributed by the user terminal;
according to the transmission delay, the weighting factor and a formula
Calculating the unloading time delay of the task to the edge server
Wherein the content of the first and second substances,
representing the transmission delay of the task on the wireless side, CiRepresenting the computational resources, epsilon, required by the taskirRepresenting a weighting factor, Br representing a set of candidate edge servers in the system, fMECRepresenting the computing resources of the edge server.
As an improvement of the above scheme, the calculation formula of the weighting factor is:
where, tmaxiThe maximum tolerant time delay of the task is shown, and p (t) shows the stay probability of the user terminal in a certain area at the moment t.
As an improvement of the above scheme, the establishing an optimization objective and a constraint condition for minimizing total energy consumption of the system model according to the transmission delay, the local delay, and the offloading delay specifically includes:
according to the local time delay and a formula
Calculating to obtain the first energy consumption of the local processing of the task
Wherein the content of the first and second substances,
representing local time delay, PiRepresenting the power of the user terminal processing task;
according to the transmission delay, the unloading delay and a formula
Calculating to obtain second energy consumption of task processing at edge server
Wherein the content of the first and second substances,
representing the offload latency of tasks to the edge server,
representing the transmission delay, P, of the task on the wireless sideMECIndicating the power of the edge server processing tasks, PtIndicating the power at which the user terminal transmits data to the edge server, P0Power representing idle state while waiting for the edge server to process the task;
according to the first energy consumption
And the second energy consumption
And establishing an optimization target and a constraint condition for minimizing the total energy consumption of the system model.
As an improvement of the above scheme, the optimization target is
The constraint condition is
Wherein the content of the first and second substances,
which indicates the current dwell time of the water,
representing the transmission delay of the task on the wireless side.
The embodiment of the invention also provides a resource allocation optimization device based on mobile edge calculation, which comprises:
the model building module is used for building a system model based on mobile edge calculation, the system model comprises at least one edge server and at least one base station, and the edge server and the base station provide calculation and communication services for all user terminals in the coverage area of the edge server and the base station;
the first calculation module is used for calculating the transmission delay of the task on the wireless side and the local delay of the task executed locally based on the system model;
the second calculation module is used for calculating the unloading time delay of the task unloaded to the edge server according to the transmission time delay, the stopping probability of the user terminal and the current stopping time;
the optimization module is used for establishing an optimization target and a constraint condition of the system model with minimized total energy consumption according to the transmission delay, the local delay and the unloading delay;
and the third calculation module is used for calculating the optimal solution of the optimization target according to the constraint condition to obtain the optimal resource allocation strategy of the system model.
The embodiment of the present invention further provides a resource allocation optimization device based on mobile edge computing, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, the processor implements any one of the above-mentioned resource allocation optimization methods based on mobile edge computing.
The embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program, and when the computer program runs, the apparatus where the computer-readable storage medium is located is controlled to execute any one of the foregoing resource allocation optimization methods based on mobile edge computing.
Compared with the prior art, the resource allocation optimization method, device and storage medium based on the mobile edge computing provided by the embodiment of the invention have the beneficial effects that: the user mobility, the unloading strategy problem and the calculation resource allocation are considered jointly, different calculation resources are allocated according to the length of the stay time of the user terminal, and the calculation resources allocated to the user terminal with shorter stay time are more, so that the requirement of the user terminal on task calculation delay in the moving process is met. In addition, the embodiment of the invention also considers the uncertainty of the movement of the user terminal, and the large uncertainty exists when the user terminal dwell time is calculated by simply adopting the current speed of the user terminal, so that the probability density function is constructed by obtaining the user terminal dwell time through big data, and the dwell probability of the user terminal at each position is reflected. And integrating the stay probability of the user terminal and the current stay time of the user terminal as a weighting factor of the edge server cluster distribution resource, and applying the weighting factor to the service processing delay of the edge server cluster. The server calculation time delay obtained based on the weighting factors is combined with the unloading strategy and the resource allocation strategy, and the resource allocation strategy can be quickly obtained by taking the total energy consumption of the system for processing the service as the optimization target.
Drawings
FIG. 1 is a flow chart illustrating a resource allocation optimization method based on mobile edge calculation according to a preferred embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a resource allocation optimizing apparatus based on mobile edge calculation according to a preferred embodiment of the present invention;
fig. 3 is a schematic structural diagram of another preferred embodiment of a resource allocation optimizing apparatus based on mobile edge calculation according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a flowchart illustrating a resource allocation optimization method based on moving edge calculation according to a preferred embodiment of the present invention. The resource allocation optimization method based on the mobile edge calculation comprises the following steps:
s1, constructing a system model based on mobile edge calculation, wherein the system model comprises at least one edge server and at least one base station, and the edge server and the base station provide calculation and communication service for all user terminals in the coverage area;
s2, calculating the transmission delay of the task on the wireless side and the local delay of the task executed locally based on the system model;
s3, calculating the unloading time delay of the task to the edge server according to the transmission time delay, the stay probability of the user terminal and the current stay time;
s4, establishing an optimization target and a constraint condition of the system model with minimized total energy consumption according to the transmission delay, the local delay and the unloading delay;
and S5, calculating the optimal solution of the optimization target according to the constraint conditions to obtain the optimal resource allocation strategy of the system model.
Specifically, the embodiment of the present invention first constructs a system model based on mobile edge computing, where the system model includes at least one edge server and at least one base station, and the edge server and the base station provide computing and communication services for all user terminals in the coverage area of the edge server and the base station. Then, the transmission delay of the task on the wireless side and the local delay of the task executed locally are calculated based on the system model. And calculating the unloading time delay of the task to the edge server according to the calculated transmission time delay, the stopping probability of the user terminal and the current stopping time, wherein the stopping probability of the user terminal is calculated by acquiring the stopping time of the user terminal through big data and constructing a probability density function. And secondly, establishing an optimization target and a constraint condition for minimizing the total energy consumption of the system model according to the transmission delay of the task on the wireless side, the local delay of the local execution of the task and the unloading delay of the unloading of the task to the edge server. And finally, calculating the optimal solution of the optimization target according to the constraint conditions, and further obtaining the optimal resource allocation strategy of the system model.
The embodiment of the invention considers the influence of user mobility on task calculation time delay, jointly considers the user mobility, the unloading strategy problem and the calculation resource allocation, and obtains the user terminal stay time through big data to construct a probability density function so as to reflect the stay probability of the user terminal at each position. The stay probability of the user terminal and the current stay time of the user terminal are integrated to be used as the weighting factor of the edge server cluster distribution resource, and the weighting factor is applied to the service delay of the edge server cluster processing, so that the requirement of the user terminal on task calculation delay in the moving process is met. The method takes the minimum total energy consumption of the system for processing the service as an optimization target, and can quickly obtain a resource allocation strategy.
In another preferred embodiment, each task triplet generated by said user terminal is used for task
Is shown in which DiIndicating the size of the task, CiIndicating the computing resources required by the task, tmaxiRepresenting the maximum tolerant delay of the task;
with the variable a being 0-1ijRepresents the way tasks are executed, namely:
wherein the content of the first and second substances,
the task i can only select a candidate edge server set Br in the system to unload or select local unloading; the set V {1,2, …, V } represents all user terminals within the coverage of the edge server and the base station; the set a {1,2, …, m } represents all edge servers of the entire system.
Specifically, in the embodiment of the present invention, the set a ═ {1,2, …, m } represents all edge servers of the entire system, and the count of each edge serverCalculating resource as fMECThe set V ═ {1,2, …, V } represents all the ues in the coverage area of the edge server and the base station, and the computing power of each ue is fi. Task triplet generated by each user terminal
Is shown in which DiIndicating the size of the task, CiIndicating the computing resources required by the task, tmaxiIndicating the largest tolerated delay for the task.
With the variable a being 0-1ijRepresents the way tasks are executed, namely:
wherein the content of the first and second substances,
indicating that the task i can only select the candidate edge server set Br in the system to unload or select local unloading.
It should be noted that the candidate edge server refers to an edge server available in the scope of the user terminal service. In mobile communication, generally, the range of one user terminal is covered by 6-14 base stations, but when the user terminal uses a service, only one main base station serves, so when the edge server of the main base station where the user terminal is located cannot receive a new task, other base stations capable of covering (the base station side hangs the edge server) receive the task of the user terminal in a wireless mode and place the task in the server of the base station side for unloading. The embodiment of the invention also provides an idea that the edge server cooperatively processes the tasks unloaded by the user terminal, and the tasks unloaded by the user terminal are not processed by a single server but are processed by a server cluster. The tasks of the user terminal can be divided into different candidate edge servers to process the tasks, Br is a candidate edge server set, and Br in different areas is different. Thus, the task processing capacity of a different region is related to the number of candidate edge servers for that region.
In another preferred embodiment, the step S2, based on the system model, of calculating a transmission delay of the task on the wireless side and a local delay of the task executed locally includes:
s201, obtaining bandwidth B and Gaussian white noise power N of an uplink channel in the system model0The transmitting power G of the user terminal, the channel fading factor h of the uplink and the average distance from the beginning to the end of the transmission task of the user terminal
S202, according to the channel fading factor h and the average distance
And formulas
Calculating to obtain a channel gain parameter Hi；
S203, according to the bandwidth B and the Gaussian white noise power N0The transmission power G, the channel gain parameter HiAnd formulas
Calculating to obtain the average data transmission rate r of the taski；
S204, according to the task size DiThe average data transmission rate riAnd formulas
Calculating to obtain the transmission time delay of the task at the wireless side
S205, according to the computing resource C needed by the taskiProcessing capacity f of the user terminaliAnd formulas
Calculating local time delay of local execution of task
Specifically, bandwidth B and Gaussian white noise power N of an uplink channel in the system model are obtained0The transmitting power G of the user terminal, the channel fading factor h of the uplink and the average distance from the beginning to the end of the transmission task of the user terminal
Since the average data transmission rate of the task is related to the channel gain parameter, the average distance is first determined according to the channel fading factor h
And formulas
Calculating to obtain a channel gain parameter Hi. Then, according to the bandwidth B of the uplink channel and the Gaussian white noise power N0Transmitting power G of user terminal and channel gain parameter HiAnd formulas
Calculating to obtain the average data transmission rate r of the taski. Then according to the task size DiAverage data transfer rate r of tasksiAnd formulas
Calculating to obtain the transmission time delay of the task at the wireless side
According to the computing resources C required by the taskiProcessing capacity f of user terminaliAnd formulas
Calculating local time delay of local execution of task
In another preferred embodiment, the step S3 of calculating, according to the transmission delay, the staying probability of the user terminal, and the current staying time, an offloading delay of offloading the task to the edge server specifically includes:
s301, a normal distribution probability density function f (x) is adopted to express the preference of the stay time of the user terminal in a certain area, namely
Wherein, mucDenotes the mean value, δcRepresents the variance;
s302, obtaining the staying probability of the user terminal in a certain area at a certain moment according to the normal distribution probability density function f (x)
Wherein x is1Is x2The last time of (c);
s303, according to the current moving speed v of the user terminaliThe distance l from the position of the user terminal in the coverage range of a certain edge server to the position of the user terminal out of the coverage range of the certain edge server and a formula
Calculating to obtain the current stay time of the user terminal at the position
S304, according to the stay probability and the current stay time
Calculating to obtain the user terminal distributionCalculating a weighting factor for the resource;
s305, according to the transmission time delay, the weighting factor and the formula
Calculating the unloading time delay of the task to the edge server
Wherein the content of the first and second substances,
representing the transmission delay of the task on the wireless side, CiRepresenting the computational resources, epsilon, required by the taskirRepresenting a weighting factor, Br representing a set of candidate edge servers in the system, fMECRepresenting the computing resources of the edge server.
Specifically, because the ue has mobility, considering the problem of the moving duration of the ue, the embodiment of the present invention allocates different computing resources according to the length of the staying time, and the shorter the staying time, the more the ue allocates the computing resources. The length of the user terminal stay time needs to consider two factors, namely the stay probability, the stay time of the user terminal in the area predicted by the current speed of the user terminal, and a weighting factor used by the edge server is calculated by integrating the stay probability and the stay time. Assuming that the residence time of the user terminal in each area is preferred, the preference of the user terminal in a certain area is expressed by a normal distribution probability density function, that is:
wherein, mucDenotes the mean value, δcThe variance is indicated.
In this embodiment, the parameter can be 0-24, muc＝10，δc0.741 denotes that the user terminal stays in a certain area for the longest time at 10 o' clock. This is only a dwell preference for a certain area, notThe dwell preference differs from region to region, i.e., the mean and variance of the formula are different. And performing simulation according to historical statistical data, and calculating the average value and the variance according to the statistical data.
Obtaining the staying probability of the user terminal in a certain area at a certain moment based on the normal distribution probability density function f (x)
Wherein x is1Is x2The last time. According to the current moving speed v of the user terminaliThe distance l from the position of the user terminal in the coverage range of a certain edge server to the position of the user terminal out of the coverage range of the certain edge server and a formula
Calculating to obtain the current stay time of the user terminal at the position
Combining the stay probability of the user terminal and the current stay time
And calculating to obtain the weighting factor of the computing resource distributed by the user terminal. Weighting factor and formula for distributing computing resource according to task transmission time delay on wireless side and user terminal
Calculating the unloading time delay of the task to the edge server
Wherein the content of the first and second substances,
representing the transmission delay of the task on the wireless side, CiRepresenting the computational resources, epsilon, required by the taskirRepresenting a weighting factor, Br representing a set of candidate edge servers in the system, fMECRepresenting the computing resources of the edge server. In thatIn the embodiment of the invention, one task can be divided into different subtasks and unloaded to different edge servers for processing, fMECIndicating the total processing capacity of the regional candidate server.
Preferably, the calculation formula of the weighting factor is:
where tmax isiThe maximum tolerant time delay of the task is shown, and p (t) shows the stay probability of the user terminal in a certain area at the moment t.
In particular, the stay probability of the user terminal and the current stay time are combined
Calculating a weighting factor of the computing resource distributed by the user terminal, wherein the calculation formula is as follows:
where, tmaxiThe maximum tolerant time delay of the task is shown, p (t) shows the stay probability of the user terminal in a certain area at the moment t, and the smaller the stay probability is, the more computing resources are distributed, and vice versa.
It should be noted that if the residence time of the user terminal in a certain area is short, i.e. less than the data transmission time, the system model does not allocate computing resources to it.
In another preferred embodiment, the S4, establishing an optimization objective and a constraint condition for minimizing the total energy consumption of the system model according to the transmission delay, the local delay and the offloading delay, specifically includes:
s401, according to the local time delay and the formula
The computationally derived task is localFirst energy consumption of
Wherein the content of the first and second substances,
representing local time delay, PiRepresenting the power of the user terminal processing task;
s402, according to the transmission time delay, the unloading time delay and the formula
Calculating to obtain second energy consumption of the task processed at the edge server
Wherein the content of the first and second substances,
representing the offload latency of tasks to the edge server,
representing the transmission delay, P, of the task on the wireless sideMECIndicating the power of the edge server processing tasks, PtIndicating the power at which the user terminal transmits data to the edge server, P0Represents the power of the idle state while waiting for the edge server to process the task;
s403, according to the first energy consumption
And the second energy consumption
And establishing an optimization target and a constraint condition for minimizing the total energy consumption of the system model.
Specifically, energy consumption facing multiple resource selection is considered when calculating energy consumption when processing task i. First, according to the local time delay and formula of the task executed locally
Calculating to obtain the first energy consumption of the local processing of the task
Wherein the content of the first and second substances,
representing local time delay, PiRepresenting the power of the user terminal processing task. Then, according to the transmission delay of the task at the wireless side, the unloading delay of the task to the edge server and the formula
Calculating to obtain second energy consumption of task processing at edge server
Wherein the content of the first and second substances,
representing the offload latency of tasks to the edge server,
representing the transmission delay, P, of the task on the wireless sideMECIndicating the power of the edge server processing task, PtIndicating the power at which the user terminal transmits data to the edge server, P0Representing the power of the idle state while waiting for the edge server to process the task. Finally, according to the first energy consumption
And a second energy consumption
And establishing an optimization target and a constraint condition for minimizing the total energy consumption of the system model.
It is to be noted that the formula
The first part represents the energy consumption of the edge service processing task, the second part represents the energy consumption of data transmission to the wireless base station, and the third part represents the energy consumption of the user terminal waiting for the task processing.
Preferably, the optimization objective is
The constraint condition is
Wherein the content of the first and second substances,
which indicates the current dwell time of the water,
representing the transmission delay of the task on the wireless side.
In particular, according to the first energy consumption
And a second energy consumption
The optimization goal of minimizing the total energy consumption for establishing the system model is
The constraint condition is
The first constraint condition is that the processing delay of each task is less than the maximum delay tolerance value; the second constraint is that the user terminal dwell time is greater than the data radio transmission time, because if the user terminal dwell time in a certain area is less than the data transmission time, then the system model will not allocate computing resources to it; the third constraint is that task i can only choose to offload candidate edge server set Br in the system or choose to offload locally.
Correspondingly, the invention also provides a resource allocation optimization device based on the mobile edge computing, which can realize all the processes of the resource allocation optimization method based on the mobile edge computing in the embodiment.
Referring to fig. 2, fig. 2 is a schematic structural diagram of a resource allocation optimization apparatus based on mobile edge computing according to a preferred embodiment of the present invention. The resource allocation optimizing device based on the mobile edge calculation comprises:
a model building module 201, configured to build a system model based on mobile edge computing, where the system model includes at least one edge server and at least one base station, and the edge server and the base station provide computing and communication services for all user terminals in their coverage areas;
a first calculating module 202, configured to calculate, based on the system model, a transmission delay of a task on a wireless side and a local delay of a task executed locally;
the second calculating module 203 is configured to calculate an unloading delay of the task to the edge server according to the transmission delay, the stop probability of the user terminal, and the current stop time;
an optimization module 204, configured to establish an optimization target and constraint conditions for minimizing total energy consumption of the system model according to the transmission delay, the local delay, and the unloading delay;
and the third calculating module 205 is configured to calculate an optimal solution of the optimization target according to the constraint condition, so as to obtain an optimal resource allocation policy of the system model.
Preferably, each task triplet generated by the user terminal is used for a task
Is shown in which DiIndicating the size of the task, CiIndicating the computing resources required by the task, tmaxiIndicating that the task is maximalDelay tolerance;
with the variable a being 0-1ijRepresents the way tasks are executed, namely:
wherein the content of the first and second substances,
the task i can only select a candidate edge server set Br in the system to unload or select local unloading; the set V {1,2, …, V } represents all user terminals within the coverage of the edge server and the base station; the set a {1,2, …, m } represents all edge servers of the entire system.
Preferably, the first calculating module 202 specifically includes:
an obtaining unit 212, configured to obtain a bandwidth B and a gaussian white noise power N of an uplink channel in the system model0The transmitting power G of the user terminal, the channel fading factor h of the uplink and the average distance from the beginning to the end of the transmission task of the user terminal
A gain calculating unit 222, configured to calculate the average distance according to the channel fading factor h
And formulas
Calculating to obtain a channel gain parameter Hi；
A transmission rate calculation unit 232 for calculating the white Gaussian noise power N according to the bandwidth B0The transmission power G, the channel gain parameter HiAnd formulas
Is calculated to obtainAverage data transmission rate r of tasksi；
A transmission delay calculating unit 242 for calculating a delay according to the task size DiThe average data transmission rate riAnd formulas
Calculating to obtain the transmission time delay of the task at the wireless side
A local delay calculating unit 252, configured to calculate the resource C according to the task requirementiProcessing capacity f of the user terminaliAnd formulas
Calculating local time delay of local execution of task
Preferably, the second calculating module 203 specifically includes:
a probability function constructing unit 213, configured to use a normal distribution probability density function f (x) to indicate the preference of the user terminal for staying in a certain area, that is, to use
Wherein, mucDenotes the mean value, δcRepresents the variance;
a staying probability calculating unit 223, configured to obtain a staying probability of the user terminal in a certain area at a certain moment according to the normal distribution probability density function f (x)
Wherein x is1Is x2The last time of (c);
a staying time calculating unit 233 for calculating the staying time according to the current moving speed v of the user terminaliThe user terminal is covered at a certain edge serverDistance l from the position of the range to the covered position and formula
Calculating to obtain the current stay time of the user terminal at the position
A weighting factor calculation unit 243 for calculating the current dwell time according to the dwell probability
Calculating to obtain a weighting factor of the computing resource distributed by the user terminal;
an offload delay calculation unit 253 for calculating an offload delay according to the transmission delay, the weighting factor and a formula
Calculating the unloading time delay of the task to the edge server
Wherein the content of the first and second substances,
representing the transmission delay of the task on the wireless side, CiRepresenting the computational resources, epsilon, required by the taskirRepresenting a weighting factor, Br representing a set of candidate edge servers in the system, fMECRepresenting the computing resources of the edge server.
Preferably, the calculation formula of the weighting factor is:
where, tmaxiThe maximum tolerant time delay of the task is shown, and p (t) shows the stay probability of the user terminal in a certain area at the moment t.
Preferably, the optimization module 204 specifically includes:
a first energy consumption calculating unit 214, configured to calculate a local time delay according to the local time delay and a formula
Calculating to obtain the first energy consumption of the local processing of the task
Wherein the content of the first and second substances,
representing local time delay, PiRepresenting the power of the user terminal processing task;
a second energy consumption calculating unit 224, configured to calculate the second energy consumption according to the transmission delay, the unloading delay, and a formula
Calculating to obtain second energy consumption of task processing at edge server
Wherein the content of the first and second substances,
representing the offload latency of tasks to the edge server,
representing the transmission delay, P, of the task on the wireless sideMECIndicating the power of the edge server processing tasks, PtIndicating the power at which the user terminal transmits data to the edge server, P0Represents the power of the idle state while waiting for the edge server to process the task;
an optimization unit 234 for optimizing the first energy consumption according to the first energy consumption
And the second energy consumption
And establishing an optimization target and a constraint condition for minimizing the total energy consumption of the system model.
Preferably, the optimization objective is
The constraint condition is
Wherein the content of the first and second substances,
which indicates the current dwell time of the water,
representing the transmission delay of the task on the wireless side.
In specific implementation, the working principle, the control flow and the technical effect of the resource allocation optimization device based on mobile edge computing according to the embodiment of the present invention are the same as those of the resource allocation optimization method based on mobile edge computing in the foregoing embodiment, and are not described herein again.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a resource allocation optimization apparatus based on mobile edge calculation according to another preferred embodiment of the present invention. The apparatus for optimizing resource allocation based on mobile edge computing comprises a processor 301, a memory 302, and a computer program stored in the memory 302 and configured to be executed by the processor 301, wherein the processor 301 implements the method for optimizing resource allocation based on mobile edge computing according to any of the above embodiments when executing the computer program.
Preferably, the computer program may be divided into one or more modules/units (e.g., computer program 1, computer program 2, … …) that are stored in the memory 302 and executed by the processor 301 to implement the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program in the resource allocation optimization device based on the moving edge calculation.
The Processor 301 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, etc., the general purpose Processor may be a microprocessor, or the Processor 301 may be any conventional Processor, the Processor 301 is a control center of the apparatus for optimizing resource allocation based on mobile edge computing, and various interfaces and lines are used to connect various parts of the apparatus for optimizing resource allocation based on mobile edge computing.
The memory 302 mainly includes a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function, and the like, and the data storage area may store related data and the like. In addition, the memory 302 may be a high speed random access memory, a non-volatile memory such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card), and the like, or the memory 302 may be other volatile solid state memory devices.
It should be noted that the above resource allocation optimization device based on mobile edge calculation may include, but is not limited to, a processor and a memory, and those skilled in the art will understand that the schematic diagram of fig. 3 is only an example of the resource allocation optimization device based on mobile edge calculation, and does not constitute a limitation of the resource allocation optimization device based on mobile edge calculation, and may include more or less components than those shown in the drawings, or combine some components, or different components.
An embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program, and when the computer program runs, a device where the computer-readable storage medium is located is controlled to execute the resource allocation optimization method based on mobile edge computing according to any of the foregoing embodiments.
The embodiment of the invention provides a resource allocation optimization method, a resource allocation optimization device and a storage medium based on mobile edge computing, which jointly consider the problems of user mobility and unloading strategies and the allocation of computing resources, allocate different computing resources according to the length of the stay time of a user terminal, and allocate more computing resources to the user terminal with shorter stay time, so that the requirement of the user terminal on task computing time delay in the moving process is met. In addition, the embodiment of the invention also considers the uncertainty of the movement of the user terminal, and the large uncertainty exists when the user terminal dwell time is calculated by simply adopting the current speed of the user terminal, so that the probability density function is constructed by obtaining the user terminal dwell time through big data, and the dwell probability of the user terminal at each position is reflected. And integrating the stay probability of the user terminal and the current stay time of the user terminal as a weighting factor of the edge server cluster distribution resource, and applying the weighting factor to the service processing delay of the edge server cluster. The server calculation time delay obtained based on the weighting factors is combined with the unloading strategy and the resource allocation strategy, and the resource allocation strategy can be quickly obtained by taking the total energy consumption of the system for processing the service as the optimization target.
It should be noted that the above-described system embodiments are merely illustrative, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the drawings of the embodiment of the system provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, and may be specifically implemented as one or more communication buses or signal lines. One of ordinary skill in the art can understand and implement it without inventive effort.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (10)
1. A resource allocation optimization method based on mobile edge computing is characterized by comprising the following steps:
constructing a system model based on mobile edge calculation, wherein the system model comprises at least one edge server and at least one base station, and the edge server and the base station provide calculation and communication services for all user terminals in the coverage area of the edge server and the base station;
calculating the transmission delay of the task at the wireless side and the local delay of the task executed locally based on the system model;
calculating the unloading time delay of the task to the edge server according to the transmission time delay, the stopping probability of the user terminal and the current stopping time;
establishing an optimization target and a constraint condition of the system model with minimized total energy consumption according to the transmission delay, the local delay and the unloading delay;
and calculating the optimal solution of the optimization target according to the constraint condition to obtain the optimal resource allocation strategy of the system model.
2. The method of claim 1, wherein each triplet for task generated by the ue is used for optimizing resource allocation based on moving edge computing
Is shown in which DiIndicating the size of the task, CiIndicating the computing resources required by the task, tmaxiRepresenting the maximum tolerant delay of the task;
using a variable of 0-1aijRepresents the way tasks are executed, namely:
wherein the content of the first and second substances,
the task i can only select a candidate edge server set Br in the system to unload or select local unloading; the set V {1,2, …, V } represents all user terminals within the coverage of the edge server and the base station; the set a {1,2, …, m } represents all edge servers of the entire system.
3. The method according to claim 1 or 2, wherein the calculating, based on the system model, a transmission delay of the task on the wireless side and a local delay of the task executed locally includes:
acquiring bandwidth B and Gaussian white noise power N of an uplink channel in the system model0The transmitting power G of the user terminal, the channel fading factor h of the uplink and the average distance from the beginning to the end of the transmission task of the user terminal
According to the channel fading factor h and the average distance
And formulas
Calculating to obtain a channel gain parameter Hi；
According to the bandwidth B and the Gaussian white noise power N0The transmission power G, the channel gain parameter HiAnd formulas
Calculating to obtain the average data transmission rate r of the taski；
According to task size DiThe average data transmission rate riAnd formulas
Calculating to obtain the transmission time delay of the task at the wireless side
According to the computing resources C required by the taskiProcessing capacity f of the user terminaliAnd formulas
Calculating local time delay of local execution of task
4. The method for optimizing resource allocation based on mobile edge computing according to claim 1, wherein the computing an offload delay of offloading a task to an edge server according to the transmission delay, a stop probability of the ue and a current stop time specifically includes:
the normal distribution probability density function f (x) is adopted to express the preference of the stay time of the user terminal in a certain area, namely
Wherein, mucDenotes the mean value, δcRepresents the variance;
obtaining the staying probability of the user terminal in a certain area at a certain moment according to the normal distribution probability density function f (x)
Wherein x is1Is x2The last time of (c);
according to the current moving speed v of the user terminaliThe distance l from the position of the user terminal in the coverage range of a certain edge server to the position of the user terminal out of the coverage range and a formula Ti s＝l/viAnd calculating to obtain the current stay time T of the user terminal at the positioni s；
According to the stay probability and the current stay time Ti sCalculating to obtain a weighting factor of the computing resources distributed by the user terminal;
according to the transmission delay, the weighting factor and a formula
Calculating the unloading time delay of the task to the edge server
Wherein the content of the first and second substances,
representing the transmission delay of the task on the wireless side, CiRepresenting the computational resources, epsilon, required by the taskirRepresenting a weighting factor, Br representing a set of candidate edge servers in the system, fMECRepresenting the computing resources of the edge server.
5. The method of claim 4, wherein the weighting factor is calculated by the following formula:
where, tmaxiRepresenting the maximum tolerant time delay of the task, and p (t) representing the user at the time tProbability of terminal stay in a certain area.
6. The method according to claim 2, wherein the establishing of the optimization objective and constraint condition for minimizing the total energy consumption of the system model according to the transmission delay, the local delay and the offloading delay specifically comprises:
according to the local time delay and a formula
Calculating to obtain the first energy consumption of the local processing of the task
Wherein the content of the first and second substances,
representing local time delay, PiRepresenting the power of the user terminal processing task;
according to the transmission delay, the unloading delay and a formula
Calculating to obtain second energy consumption of task processing at edge server
Wherein the content of the first and second substances,
representing the offload latency of tasks to the edge server,
representing the transmission delay, P, of a task on the wireless sideMECIndicating the power of the edge server processing tasks, PtIndicating the power at which the user terminal transmits data to the edge server, P0Representing waiting edge serversPower of idle state while processing tasks;
according to the first energy consumption
And the second energy consumption
And establishing an optimization target and a constraint condition for minimizing the total energy consumption of the system model.
7. The method of claim 6, wherein the optimization objective is to optimize the resource allocation based on the moving edge calculation
The constraint condition is
Wherein, Ti sWhich indicates the current dwell time of the water,
representing the transmission delay of the task on the wireless side.
8. An apparatus for optimizing resource allocation based on mobile edge computing, comprising:
the model building module is used for building a system model based on mobile edge calculation, the system model comprises at least one edge server and at least one base station, and the edge server and the base station provide calculation and communication services for all user terminals in the coverage area of the edge server and the base station;
the first calculation module is used for calculating the transmission delay of the task on the wireless side and the local delay of the task executed locally based on the system model;
the second calculation module is used for calculating the unloading time delay of the task unloaded to the edge server according to the transmission time delay, the stopping probability of the user terminal and the current stopping time;
the optimization module is used for establishing an optimization target and a constraint condition for minimizing the total energy consumption of the system model according to the transmission delay, the local delay and the unloading delay;
and the third calculation module is used for calculating the optimal solution of the optimization target according to the constraint condition to obtain the optimal resource allocation strategy of the system model.
9. A mobile edge computing-based resource allocation optimization apparatus, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the mobile edge computing-based resource allocation optimization method according to any one of claims 1 to 7.
10. A computer-readable storage medium, comprising a stored computer program, wherein the computer program, when running, controls an apparatus in which the computer-readable storage medium is located to perform the method for optimizing resource allocation based on mobile edge computing according to any one of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115022331A (en) * | 2022-05-30 | 2022-09-06 | 中国电信股份有限公司 | Edge computing resource allocation method and device, storage medium and electronic equipment |
| CN115022189A (en) * | 2022-05-31 | 2022-09-06 | 武汉大学 | Edge user distribution model construction method, device, equipment and readable storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115022331A (en) * | 2022-05-30 | 2022-09-06 | 中国电信股份有限公司 | Edge computing resource allocation method and device, storage medium and electronic equipment |
| CN115022331B (en) * | 2022-05-30 | 2024-05-14 | 中国电信股份有限公司 | Edge computing resource allocation method and device, storage medium and electronic equipment |
| CN115022189A (en) * | 2022-05-31 | 2022-09-06 | 武汉大学 | Edge user distribution model construction method, device, equipment and readable storage medium |
| CN115022189B (en) * | 2022-05-31 | 2024-03-26 | 武汉大学 | Edge user allocation model construction method, device, equipment and readable storage medium |
| CN115827185A (en) * | 2022-10-31 | 2023-03-21 | 中电信数智科技有限公司 | 6G aerial base station and Beidou aerial obstacle avoidance combined method, storage medium and equipment |
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| CN116560839A (en) * | 2023-05-06 | 2023-08-08 | 湖南师范大学 | Edge computing task unloading method and system based on master-slave game |
| CN116560839B (en) * | 2023-05-06 | 2023-11-10 | 湖南师范大学 | Edge computing task unloading method and system based on master-slave game |
| CN117873689A (en) * | 2024-03-11 | 2024-04-12 | 浪潮计算机科技有限公司 | Task allocation method, device, equipment and computer readable storage medium |
| CN117873689B (en) * | 2024-03-11 | 2024-05-31 | 浪潮计算机科技有限公司 | Task allocation method, device, equipment and computer readable storage medium |
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